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    • In order to analyze and illustrate Table Figs steepness of

    2018-11-05

    In order to analyze and illustrate (Table 1, Figs. 2–3) steepness of changes in the EEG indexes during transitions from one stage to another, one-min values were further averaged on 3-min intervals (i.e., from minute −4 to −2, from −1 to +1, from +2 to +4, etc.). Additionally, all positive and negative one-min scores on a component (i.e., 1st or 4th) were transformed into +1 and 0, respectively, for illustrating time courses of probability of association of a stage (i.e., 2 or 3) with positive score on a component (Fig. 4). One-way repeated-measure analyses of variance (rANOVAs) with within-subjects factor “Three-min interval” were performed for examining whether an EEG index ehibites statistically significant changes both prior to and after onset of a stage. The Bonferroni multiple comparison test was used in the post hoc analysis, i.e., for examining significance of differences between a value of an index for 0-minute of stage onset and the preceding and following values (Table 1). Additionally, 2-way ANOVAs were run on time courses of the EEG indexes (Fig. 2) with fixed factor “Interval”, i.e., either “Three-min interval” (Fig. 3B) or “One-min interval” (Fig. 4), and random factor “Participant” (Fig. 3A). Finally, two-way ANOVAs with two fixed factors were conducted on these time courses to test whether they buy GDC0068 are different for different experimental conditions, times of day, delays of stage onset, etc. The first fixed factors was “Three-min interval” and the second was, respectively, “Condition” (sleep deprived vs. slept), “Time of day” (12 clock times; Fig. 3A), “Stage onset delay” (minutes −12, −9, −6, −3, and 0), etc.
    Results Principal component analysis yielded the first four eigenvalues that were either remarkably higher than 1 (the 1st and 2nd) or close to 1 (the 3rd and 4th). These four principal components collectively explained for almost 84% of the total variance of the EEG spectrum (55%, 18%, 6%, and 5%, respectively). The shapes of principal component loadings are illustrated in Fig. 1 by plotting the loadings of the 16 powers on each component as a function of frequency band. The shapes of such loading spectra obtained for the whole dataset were very similar to those obtained for any study participant (Fig. 1A and B, respectively). Therefore, only scores calculated for the whole dataset were used in further analysis. Fig. 2 demonstrates that time courses of spectral powers and principal component scores were often characterized by very steep changes either before or after or both before and after onset of a certain sleep stage. Table 1 reveals that such changes cannot be regarded as being stage-specific in the case of low frequency powers (i.e., delta and theta) that always dominated during well-established sleep. For instance, delta power exhibited steep rises both before and after onsets of all three stages. In contrast, steep changes in high frequency powers (i.e., alpha and sigma) were specifically linked to onset of a certain stage, i.e., they showed steep rises both before and after onset of only one of three stages. As one can predict from the conventional sleep scoring rules, the steep decline of alpha power occurred around sleep onset (i.e., mutation reflects the phenomenon of attenuation of alpha rhythm during transition from wakefulness to stage 1 sleep), whereas sigma power exhibited its steep rise around onset of stage 2 sleep (i.e., it is associated with typical short sequences of waves of 11–15Hz called “sleep spindles”). Table 1 and Fig. 2B indicate that rapid changes in scores on the 1st, 2nd, and 4th principal components were linked to onset of only one of three stages (2, 1, and 3, respectively), and that the transition from one stage to another was associated with a change in the sign of score (from positive to negative for 2nd score and from negative to positive for 1st and 4th score). Fig. 3A illustrates that the records obtained for 12 different clock times were similar on the time courses of scores on the 1st, 2nd, and 4th principal components around onsets of stages 2, 1, and 3, respectively. As indicated by the results of two-way ANOVAs with the first factor “Three-min interval” and the second factor either “Condition” (sleep deprived vs. slept) or “Time of day” (12 clock times; Fig. 3A) or “Delay of stage onset” (minutes −12, −9, −6, −3, and 0), neither significant main effect of the second factor nor interaction between the two factors were significant. Fig. 3B illustrates remarkable similarity between participants on the time courses on scores on the 1st, 2nd, and 4th principal components. For instance, 4th score always become positive when sleep of a given study participant progressed into SWS.